CN104616667B - A kind of active denoising method in automobile - Google Patents
A kind of active denoising method in automobile Download PDFInfo
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Abstract
The present invention relates to a kind of active denoising method in automobile, belong to voice process technology field.This active denoising method is by introducing secondary sound source, the acoustical signal for controlling secondary sound source to send using adaptive algorithm, makes the anti-phase purpose to reach fixed point noise reduction of noise constant amplitude of the secondary sound wave exported after adaptive convergence in noise reduction point just with the point.Improvement of the present invention on the basis of original active denoising method by algorithm with method structure in itself, least-mean-square error algorithm is substituted using recursive least squares as main path transfer function to estimate and time core adaptive algorithm of path transmission Function Estimation, make this method that there is very strong elimination impulsive noise and nonstationary noise ability and good noise reduction error and noise reduction speed, simultaneously to introducing in-car stick signal, improve the stability problem caused by signal correlation, and realize and retain useful signal while noise reduction, greatly improve in-car signal to noise ratio.
Description
Technical field
The present invention relates to a kind of active denoising method in automobile, belong to voice process technology field.
Background technology
The Noise measarement in Automobile mainly uses traditional passive by sound insulation, vibration isolation, noise elimination, sound absorption etc. a few days ago
Noise reduction technology carries out noise reduction, and these technologies are obvious to the in-car medium, high frequency noise effects of reduction, but just not very managed for low-frequency noise
Think.And active noise reduction techniques are reducing low frequency due to the characteristics of it can be effectively reduced low-frequency noise, being subject to the people's attention
Gradually it is employed in noise.Active noise reduction principle is, by an electroacoustics system, to produce one and source noise polarity inversion, intensity
Equal new sound-source signal, with the signal and source noise Signal averaging, realizes the counteracting of source noise.This method is with strong points,
The transmission of speech signal can be ensured while noise reduction, the Noise measarement in larger space can be realized.
But existing method uses least-mean-square error algorithm for the algorithm of basic sef-adapting filter, although there is calculation
Method complexity is low, it is easy to hard-wired feature, but it is influenceed algorithm the convergence speed slow by secondary path effects, and stability is not high,
The problem of reply mutation noise is unable to do what one wishes so that active denoising method existing defects in practical application.
The content of the invention
The purpose of the present invention is to propose to be to propose a kind of active denoising method in automobile, by using more stable
Effective recursive least squares and improved secondary path method of estimation are to the noise control method in existing Automobile
It is improved, improves noise reduction speed, noise reduction and the stability of active denoising method.
Active denoising method proposed by the present invention in automobile, comprises the following steps：
(1) transfer function for defining noise inside automobile source to human ear path is main tunnel function ω (n), to primary path
The predicted value of transfer function isSettingInitial valueWherein N is sampling period sequence number, and n is filtering
Exponent number, the transfer function for defining secondary sound source to human ear path in automobile is secondary path transfer function S (n), to secondary path
Transfer function predicted value isSettingInitial valueIt is set in solution primary path transfer function predicted valueWhen intermediate variable based on update matrix Initial valueWherein u1For on the occasion of normal
Number, u1Span be 0-0.5, I be n rank unit matrixs, n is filter order, be set in solution secondary path transfer function
Predicted valueWhen intermediate variable update matrix to be secondary Initial valueWherein u is
Positive constant, u span is 0-0.5, and sampling period sequence number N carries out following cycle calculations since 1：
(2) microphone is set near the noise source in automobile, and microphone collection environment inside car noise will be current
Moment, the environment inside car noise set a microphone, the Mike in the car as reference signal x (N) near the ear of passenger
Voice signal near elegance collection ear, will current time the voice signal as error signal e (N), N is sampling period sequence
Number；
(3) the iteration reference signal for defining primary path transmission filter vector version is x dimensional vector x (n), x (n)=[x (N) x
(N-1)......x(N-n+1)]T, n is filter order, and wherein x (N) is the reference signal obtained in the n-th sampling period, x (N-
1) reference signal obtained for the N-1 sampling period, the rest may be inferred by analogy for it, the iteration reference signal x (n) is carried out by following formula pre-
Processing, obtains pretreatment reference signal x ' (N),Wherein,ForTransposition,
For the secondary path transfer function predicted value in n-th sampling period,Computational methods comprise the following steps：
The iteration output signal that (3-1) defines secondary path transmission filter vector version is n-dimensional vector y (n), y (n)=[y
(N-1)y(N-2)......y(N-n)]T, n is filter order, and wherein y (N-1) is the output letter obtained in the N-1 sampling period
Number, y (N-2) is the output signal obtained in the N-2 sampling period, and the rest may be inferred by analogy for it, calculates secondary using iteration output signal y (n)
Stage gain factor k (n),Wherein λ is the convergence constant of setting, λ span
For 0-1,It is that the N-1 the secondary of sampling period updates matrix, y (n) is by leading to above-mentioned reference signal x (N)
The output signal of dynamic noise reduction；
(3-2) is filtered to above-mentioned iteration output signal y (n), and filter factor leads to for the secondary in previous sampling period
Road transfer function predicted valueObtain one-level noise reduction filtering signalWhereinForTransposition；
(3-3) is by above-mentioned error signal e (N) and above-mentioned one-level noise reduction filtering signal y1(N) subtract each other, obtain one-level error letter
Number e1(N), e1(N)=e (N)-y1(N)；
(3-4) calculates secondary path transfer function predicted value according to step (3-1) secondary gain factor k (n) Wherein,It is e1(N) complex conjugate, e1(N) be step (3-3) one-level miss
Difference signal；
(3-5) according to above-mentioned steps (3-1) secondary gain factor k (n), calculate obtain current sample period it is secondary more
New matrixWhereinIt is n rank matrixes,It is that the N-1 the secondary of sampling period updates matrix, λ is the convergence constant of setting, and λ span is 0-1, yT
(n) be y (n) transposition, y (n) is above-mentioned iteration output signal；
(4) the pretreatment iteration reference signal of definition vector form is n-dimensional vector x ' (n), x ' (n)=[x ' (N) x ' (N-
1)......x′(N-n+1)]T, n is filter order, and wherein x ' (N) is the output signal obtained in the n-th sampling period, x ' (N-
1) output signal obtained for the N-1 sampling period, the rest may be inferred by analogy for it, using the pretreatment iteration reference signal x ' (n), calculates
Master gain factor k1(n),Wherein, λ1It is the convergence constant of setting, λ1Take
Value scope is 0-1,It is the main renewal matrix in the N-1 sampling period, x ' (n) is above-mentioned pretreatment reference signal；
(5) horizontal filtering is carried out to the iteration reference signal x (n) of above-mentioned steps (3) using following formula, obtains one-level filtering letter
Number y ' (N),Wherein,It isTransposition,It is the master of current sample period
Tunnel function prediction value；
(6) the one-level filtering signal y ' (N) of step (5) is superimposed with automotive interior stick signal T (N), obtains output letter
Number y (N)=- y ' (N)+T (N)
(7) according to the master gain factor k of above-mentioned steps (4)1(n) above-mentioned primary path transfer function predicted value, is calculated Wherein, e (N) is the error signal in step (2)；
(8) according to the master gain factor k of step (4)1(n) the main renewal matrix in currently employed cycle, is calculated WhereinIt is n rank matrixes,It is
The main renewal matrix in N-1 sampling period, λ1It is the convergence constant of setting, λ1Span be 0-1, x ' T (n) are x's ' (n)
Transposition, x ' (n) is the pretreatment reference signal in step (4)；
(9) sampling period number N=N+1, repeat step (2)-step (9) are made.
Active denoising method proposed by the present invention in automobile, its advantage is：Main path transfer function is estimated and secondary
The core adaptive algorithm of path transmission Function Estimation uses recursive least squares, and it is missed compared to existing lowest mean square
Difference algorithm has a convergence rate quickly, very strong elimination impulsive noise and nonstationary noise ability and good convergence error and
Stability；The signal for output signal being employed in the estimation procedure of secondary path transfer function with in-car stick signal is superimposed is made
For the parameter of estimation, output signal and input reference signal correlation are reduced, is greatly reduced caused by signal correlation
The stability problem of secondary path algorithm for estimating.
Brief description of the drawings
Fig. 1 is the FB(flow block) of the inventive method.
Embodiment
Active denoising method proposed by the present invention in automobile, its FB(flow block) is as shown in figure 1, including following step
Suddenly：
(1) transfer function for defining noise inside automobile source to human ear path is main tunnel function ω (n), to primary path
The predicted value of transfer function isSettingInitial valueWherein N is sampling period sequence number, and n is filtering
Exponent number, the transfer function for defining secondary sound source to human ear path in automobile is secondary path transfer function S (n), to secondary path
Transfer function predicted value isSettingInitial valueIt is set in solution primary path transfer function predicted valueWhen intermediate variable based on update matrix Initial valueWherein u1For on the occasion of normal
Number, u1Span be 0-0.5, I be n rank unit matrixs, n is filter order, be set in solution secondary path transfer function
Predicted valueWhen intermediate variable update matrix to be secondaryInitial valueWherein u is
Positive constant, u span is 0-0.5, and sampling period sequence number N carries out following cycle calculations since 1：
(2) microphone is set near the noise source in automobile, and microphone collection environment inside car noise will be current
Moment, the environment inside car noise set a microphone, the Mike in the car as reference signal x (N) near the ear of passenger
Voice signal near elegance collection ear, will current time the voice signal as error signal e (N), N is sampling period sequence
Number；
(3) the iteration reference signal for defining primary path transmission filter vector version is n-dimensional vector x (n), x (n)=[x (N) x
(N-1)......x(N-n+1)]T, n is filter order, and wherein x (N) is the reference signal obtained in the n-th sampling period, x (N-
1) reference signal obtained for the N-1 sampling period, the rest may be inferred by analogy for it, the iteration reference signal x (n) is carried out by following formula pre-
Processing, obtains pretreatment reference signal x ' (N),Wherein,ForTransposition,
For the secondary path transfer function predicted value in n-th sampling period,Computational methods comprise the following steps：
The iteration output signal that (3-1) defines secondary path transmission filter vector version is n-dimensional vector y (n), y (n)=[y
(N-1)y(N-2)......y(N-n)]T, n is filter order, and wherein y (N-1) is the output letter obtained in the N-1 sampling period
Number, y (N-2) is the output signal obtained in the N-2 sampling period, and the rest may be inferred by analogy for it, calculates secondary using iteration output signal y (n)
Stage gain factor k (n),Wherein λ is the convergence constant of setting, λ span
For 0-1,It is that the N-1 the secondary of sampling period updates matrix, y (n) is by leading to above-mentioned reference signal x (N)
The output signal of dynamic noise reduction；
(3-2) is filtered to above-mentioned iteration output signal y (n), and filter factor leads to for the secondary in previous sampling period
Road transfer function predicted valueObtain one-level noise reduction filtering signalWhereinForTransposition；
(3-3) is by above-mentioned error signal e (N) and above-mentioned one-level noise reduction filtering signal y1(N) subtract each other, obtain one-level error letter
Number e1(N), e1(N)=e (N)-y1(N)；
(3-4) calculates secondary path transfer function predicted value according to step (3-1) secondary gain factor k (n)Wherein,It isComplex conjugate, e1(N) it is step (3-3)
One-level error signal；
(3-5) according to above-mentioned steps (3-1) secondary gain factor k (n), calculate obtain current sample period it is secondary more
New matrixWhereinIt is n rank matrixes,It is that the N-1 the secondary of sampling period updates matrix, λ is the convergence constant of setting, and λ span is 0-1,Transposition, y (n) is above-mentioned iteration output signal；
(4) the pretreatment iteration reference signal of definition vector form is n-dimensional vector x ' (n), x ' (n)=[x ' (N) x ' (N-
1)......x′(N-n+1)]T, n is filter order, and wherein x ' (N) is the output signal obtained in the n-th sampling period, x ' (N-
1) output signal obtained for the N-1 sampling period, the rest may be inferred by analogy for it, using the pretreatment iteration reference signal x ' (n), calculates
Master gain factor k1(n),Wherein, λ1It is the convergence constant of setting, λ1Take
Value scope is 0-1,It is the main renewal matrix in the N-1 sampling period, x ' (n) is above-mentioned pretreatment reference signal；
(5) horizontal filtering is carried out to the iteration reference signal x (n) of above-mentioned steps (3) using following formula, obtains one-level filtering letter
Number y ' (N),Wherein,It isTransposition,Be current sample period master lead to
Road transfer function predicted value；
(6) the one-level filtering signal y ' (N) of step (5) is superimposed with automotive interior stick signal T (N), obtains output letter
Number y (N)=- y ' (N)+T (N), automotive interior stick signal therein can be music or voice signal for playing etc.；
(7) according to the master gain factor k of above-mentioned steps (4)1(n) above-mentioned primary path transfer function predicted value, is calculated Wherein, e (N) is the error signal in step (2)；
(8) according to the master gain factor k of step (4)1(n) the main renewal matrix in currently employed cycle, is calculated WhereinIt is n rank matrixes,It is
The main renewal matrix in N-1 sampling period, λ1It is the convergence constant of setting, λ1Span be 0-1, x 'T(n) it is x's ' (n)
Transposition, x ' (n) is the pretreatment reference signal in step (4)；
(9) sampling period number N=N+1, repeat step (2)-step (9) are made.
The operation principle of the inventive method introduced below：
This active denoising method is restrained by algorithm iteration by introducing secondary sound source and calculates to control secondary sound source to send
Acoustical signal, make the secondary sound wave of output noise reduction point just with the anti-phase purpose to reach fixed point noise reduction of the spot noise constant amplitude.
In the noise-reduction method of the present invention, iterative process can tend to convergence, definition in a short period of timeIt is primary path transfer function
Predicted valueZ-transform, after short time iterationA stationary value will be tended to,Its
Middle W (z) is the z-transform of actual primary path transfer function w (n),It is secondary path transfer function predicted valueZ-transform,
SimultaneouslyAlso a stationary value will be tended to,Wherein S (z) is that the z of actual secondary path transfer function s (n) becomes
Change.The signal that last human ear is actually hearing Wherein E (z) is actual error signal e (n) z-transform, and the z that X (z) is reference signal x (n) becomes
Change, during Y (z) is output signal y (n) z-transform, the signal e (n) being finally actually hearing, noise signal x (n) disappears, only remaining
The signal related to the stick signal T (n) that system is exported, so realize noise reduction.
Claims (1)
1. a kind of active denoising method in automobile, it is characterised in that this method comprises the following steps：
(1) transfer function for defining noise inside automobile source to human ear path is main tunnel function ω (n), and primary path is transmitted
The predicted value of function isSettingInitial valueWherein N is sampling period sequence number, and n is filtering rank
Number, it is secondary path transfer function S (n) to the transfer function in human ear path to define secondary sound source in automobile, and secondary path is passed
Defeated function prediction value isSettingInitial valueIt is set in solution primary path transfer function predicted valueWhen intermediate variable based on update matrix Initial valueWherein u1For on the occasion of normal
Number, u1Span be 0-0.5, I be n rank unit matrixs, n is filter order, be set in solution secondary path transfer function
Predicted valueWhen intermediate variable update matrix to be secondary Initial valueWherein u is
Positive constant, u span is 0-0.5, and sampling period sequence number N carries out following cycle calculations since 1：
(2) microphone, microphone collection environment inside car noise, by current time are set near the noise source in automobile
The environment inside car noise sets a microphone, Mike's elegance in the car as reference signal x (N) near the ear of passenger
Collect the voice signal near ear, will current time the voice signal as error signal e (N), N is sampling period sequence number；
(3) the iteration reference signal for defining primary path transmission filter vector version is n-dimensional vector x (n), x (n)=[x (N) x (N-
1)……x(N-n+1)]T, n is filter order, and wherein x (N) is n-th sampling period obtained reference signal, and x (N-1) is the
The reference signal that N-1 sampling period obtains, the rest may be inferred by analogy for it, and the iteration reference signal x (n) is pre-processed by following formula,
Pretreatment reference signal x ' (N) is obtained,Wherein,ForTransposition,For N
The secondary path transfer function predicted value in individual sampling period,Computational methods comprise the following steps：
The iteration output signal that (3-1) defines secondary path transmission filter vector version is n-dimensional vector y (n), y (n)=[y (N-
1)y(N-2)……y(N-n)]T, n is filter order, and wherein y (N-1) is the output signal obtained in the N-1 sampling period, y
(N-2) output signal obtained for the N-2 sampling period, the rest may be inferred by analogy for it, and secondary increase is calculated using iteration output signal y (n)
Beneficial factor k (n),Wherein λ is the convergence constant of setting, and λ span is 0-
1,It is that the N-1 the secondary of sampling period updates matrix, y (n) is by actively dropping to above-mentioned reference signal x (N)
The output signal made an uproar；
(3-2) is filtered to above-mentioned iteration output signal y (n), and filter factor passes for the secondary path in previous sampling period
Defeated function prediction valueObtain one-level noise reduction filtering signalWhereinForTransposition；
(3-3) is by above-mentioned error signal e (N) and above-mentioned one-level noise reduction filtering signal y1(N) subtract each other, obtain one-level error signal e1
(N), e1(N)=e (N)-y1(N)；
(3-4) calculates secondary path transfer function predicted value according to step (3-1) secondary gain factor k (n) Wherein,It is e1(N) complex conjugate, e1(N) be step (3-3) one-level miss
Difference signal；
(3-5) calculates the secondary renewal matrix for obtaining current sample period according to above-mentioned steps (3-1) secondary gain factor k (n) WhereinIt is n rank matrixes,
It is that the N-1 the secondary of sampling period updates matrix, λ is the convergence constant of setting, and λ span is 0-1, yT(n) it is y
(n) transposition, y (n) is above-mentioned iteration output signal；
(4) the pretreatment iteration reference signal of definition vector form is n-dimensional vector x ' (n), x ' (n)=[x ' (N) x ' (N-
1)……x′(N-n+1)]T, n is filter order, and wherein x ' (N) is the output signal obtained in the n-th sampling period, x ' (N-1)
The output signal obtained for the N-1 sampling period, the rest may be inferred by analogy for it, using the pretreatment iteration reference signal x ' (n), calculates master
Gain factor k1(n),Wherein, λ1It is the convergence constant of setting, λ1Value
Scope is 0-1,It is the main renewal matrix in the N-1 sampling period, x ' (n) is above-mentioned pretreatment reference signal；
(5) horizontal filtering is carried out to the iteration reference signal x (n) of above-mentioned steps (3) using following formula, obtains one-level filtering signal y '
(N),Wherein,It isTransposition,It is the primary path biography of current sample period
Defeated function prediction value；
(6) the one-level filtering signal y ' (N) of step (5) is superimposed with automotive interior stick signal T (N), obtains output signal y
(N)=- y ' (N)+T (N), described automotive interior stick signal is the music or voice signal played；
(7) according to the master gain factor k of above-mentioned steps (4)1(n) above-mentioned primary path transfer function predicted value, is calculated Wherein, e (N) is the error signal in step (2)；
(8) according to the master gain factor k of step (4)1(n) the main renewal matrix in currently employed cycle, is calculated WhereinIt is n rank matrixes,It is
The main renewal matrix in N-1 sampling period, λ1It is the convergence constant of setting, λ1Span be 0-1, x 'T(n) it is x's ' (n)
Transposition, x ' (n) is the pretreatment reference signal in step (4)；
(9) sampling period number N=N+1, repeat step (2)-step (9) are made.
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Families Citing this family (28)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9704509B2 (en) * | 2015-07-29 | 2017-07-11 | Harman International Industries, Inc. | Active noise cancellation apparatus and method for improving voice recognition performance |
| CN105788604B (en) * | 2016-04-07 | 2019-07-12 | 会听声学科技（北京）有限公司 | A kind of optimization active denoising method based on FXLMS |
| CN106128449B (en) * | 2016-08-16 | 2023-09-01 | 青岛歌尔声学科技有限公司 | Active noise reduction method for automobile |
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| CN108538304B (en) * | 2018-03-09 | 2021-10-01 | 华侨大学 | Active control system for noise in vehicle |
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| JP7254935B2 (en) * | 2018-12-19 | 2023-04-10 | グーグル エルエルシー | Robust adaptive noise cancellation system and method |
| CN111627414B (en) * | 2019-02-28 | 2024-01-30 | 上海汽车集团股份有限公司 | Active denoising method and device and electronic equipment |
| CN112151001B (en) * | 2019-06-26 | 2023-11-14 | 广州汽车集团股份有限公司 | PHEV active noise reduction method and system thereof |
| CN112141076B (en) * | 2019-06-26 | 2022-01-21 | 广州汽车集团股份有限公司 | Method and system for improving ANC noise reduction performance of vehicle idling and automobile |
| CN111128198B (en) * | 2019-12-25 | 2022-10-28 | 厦门快商通科技股份有限公司 | Voiceprint recognition method, voiceprint recognition device, storage medium, server and voiceprint recognition system |
| CN112562626B (en) * | 2020-11-30 | 2021-08-31 | 深圳百灵声学有限公司 | Design method of hybrid noise reduction filter, noise reduction method, system and electronic equipment |
| CN113053406A (en) * | 2021-05-08 | 2021-06-29 | 北京小米移动软件有限公司 | Sound signal identification method and device |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101231846A (en) * | 2007-12-27 | 2008-07-30 | 中国农业大学 | Initiative noise control system and noise control method using sound wave interference mode |
| CN101740023A (en) * | 2008-11-20 | 2010-06-16 | 哈曼国际工业有限公司 | Active noise control system with audio signal compensation |
| CN102026080A (en) * | 2009-04-02 | 2011-04-20 | 奥迪康有限公司 | Adaptive feedback cancellation based on inserted and/or intrinsic characteristics and matched retrieval |
| CN102159426A (en) * | 2008-09-18 | 2011-08-17 | 本田技研工业株式会社 | Active noise control device |
| CN102209987A (en) * | 2008-11-24 | 2011-10-05 | 高通股份有限公司 | Systems, methods, apparatus, and computer program products for enhanced active noise cancellation |
| CN103035234A (en) * | 2011-09-28 | 2013-04-10 | 比亚迪股份有限公司 | Device and method of active noise reduction and noise controlling system used for smoke exhaust ventilator |
| EP2629289A1 (en) * | 2012-02-15 | 2013-08-21 | Harman Becker Automotive Systems GmbH | Feedback active noise control system with a long secondary path |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8718291B2 (en) * | 2011-01-05 | 2014-05-06 | Cambridge Silicon Radio Limited | ANC for BT headphones |
-
2014
- 2014-12-02 CN CN201410722895.XA patent/CN104616667B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101231846A (en) * | 2007-12-27 | 2008-07-30 | 中国农业大学 | Initiative noise control system and noise control method using sound wave interference mode |
| CN102159426A (en) * | 2008-09-18 | 2011-08-17 | 本田技研工业株式会社 | Active noise control device |
| CN101740023A (en) * | 2008-11-20 | 2010-06-16 | 哈曼国际工业有限公司 | Active noise control system with audio signal compensation |
| CN102209987A (en) * | 2008-11-24 | 2011-10-05 | 高通股份有限公司 | Systems, methods, apparatus, and computer program products for enhanced active noise cancellation |
| CN102026080A (en) * | 2009-04-02 | 2011-04-20 | 奥迪康有限公司 | Adaptive feedback cancellation based on inserted and/or intrinsic characteristics and matched retrieval |
| CN103035234A (en) * | 2011-09-28 | 2013-04-10 | 比亚迪股份有限公司 | Device and method of active noise reduction and noise controlling system used for smoke exhaust ventilator |
| EP2629289A1 (en) * | 2012-02-15 | 2013-08-21 | Harman Becker Automotive Systems GmbH | Feedback active noise control system with a long secondary path |
Non-Patent Citations (1)
| Title |
|---|
| "舰艇自噪声自适应有源抵消技术研究";鲍雪山;《中国博士学位论文全文数据库信息科技辑》;20100615;全文 * |
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